Python Machine Learning - Code Examples

Chapter 4: Building Good Training Datasets – Data Preprocessing

Chapter Outline

• Dealing with missing data

  • Identifying missing values in tabular data

  • Eliminating training examples or features with missing values

  • Imputing missing values

  • Understanding the scikit-learn estimator API

• Handling categorical data

  • Nominal and ordinal features

  • Creating an example dataset

  • Mapping ordinal features

  • Encoding class labels

  • Performing one-hot encoding on nominal features

• Partitioning a dataset into separate training and test sets

• Bringing features onto the same scale

• Selecting meaningful features

  • L1 and L2 regularization as penalties against model complexity

  • A geometric interpretation of L2 regularization

  • Sparse solutions with L1 regularization

  • Sequential feature selection algorithms

• Assessing feature importance with random forests

• Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.